An Analysis of the Differences in Overseas Buyers’ Perception on Korean Consumer Products
Bibliographic record
Abstract
Background/Objectives: This study is about the difference of perception among overseas buyers who are interested in Korean consumer goods. The purpose of this study is to analyze whether there is a difference in price, quality, differentiation (uniqueness), reliability, and post-consultation intention for buyers to purchase representative consumer products in Korea.Methods/Statistical analysis: For the research method, we surveyed 420 overseas buyers who participated in the SME Consumer Goods Product Purchasing Meeting held in Seoul in 2019. 660 valid surveys were collected from infant, cosmetic, fashion, food and household buyers in Southeast Asia, China, Japan and other regions (Europe, USA, CIS). With SPSS22.0, we analyzed exploratory factors and reliability of Korean buyers’ characteristics and overseas buyers’ evaluation criteria for Korean companies and products. And, ANOVA was used to examine the difference in buyers’ awareness of consumer goods and product characteristic.Findings: According to the analysis results, there is a difference in recognition of price and trading intentions for the product group (baby goods, cosmetics, fashion goods, food and household goods, etc.) regardless of the buyer’s region. Therefore, price correlates with buying decisions and it can be seen that price influences trading decisions. However, there is no difference in perception in quality, differentiation and reliability, and we can see that the quality of consumer products in Korea is generally well received and high in reliability. On the other hand, buyers from different regions showed differences in perception in price, quality, and reliability, but there were no perception differences in product differentiation or intention to trade. Therefore, although prices, quality, and reliability are evaluated differently, it can be seen that there is a high willingness to trade in acknowledgment of product differentiation or uniqueness.Improvements/Applications: In the future, it is hoped that the research will be conducted only for buyers in Southeast Asian countries, and through this, it will be possible to establish marketing strategies by analyzing buyer tendency of each country in Southeast Asian countries. In addition, we hope to analyze more specific marketing points through research linking buyers’ evaluations with the performance of domestic companies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".